Overview

Dataset statistics

Number of variables14
Number of observations350378
Missing cells53629
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory37.4 MiB
Average record size in memory112.0 B

Variable types

Categorical1
DateTime3
Numeric9
Text1

Alerts

VERSIE has constant value ""Constant
DATUM_BESTAND has constant value ""Constant
PEILDATUM has constant value ""Constant
AANTAL_PAT_PER_DIAG is highly overall correlated with AANTAL_SUBTRAJECT_PER_DIAGHigh correlation
AANTAL_PAT_PER_SPC is highly overall correlated with AANTAL_SUBTRAJECT_PER_SPC and 1 other fieldsHigh correlation
AANTAL_PAT_PER_ZPD is highly overall correlated with AANTAL_SUBTRAJECT_PER_ZPDHigh correlation
AANTAL_SUBTRAJECT_PER_DIAG is highly overall correlated with AANTAL_PAT_PER_DIAGHigh correlation
AANTAL_SUBTRAJECT_PER_SPC is highly overall correlated with AANTAL_PAT_PER_SPCHigh correlation
AANTAL_SUBTRAJECT_PER_ZPD is highly overall correlated with AANTAL_PAT_PER_ZPDHigh correlation
BEHANDELEND_SPECIALISME_CD is highly overall correlated with AANTAL_PAT_PER_SPCHigh correlation
GEMIDDELDE_VERKOOPPRIJS has 53629 (15.3%) missing valuesMissing
AANTAL_SUBTRAJECT_PER_ZPD is highly skewed (γ1 = 21.20565322)Skewed

Reproduction

Analysis started2024-02-26 14:43:06.811509
Analysis finished2024-02-26 14:43:26.242606
Duration19.43 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

VERSIE
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
1.0
350378 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1051134
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 350378
100.0%

Length

2024-02-26T14:43:26.338202image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-26T14:43:26.480448image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 350378
100.0%

Most occurring characters

ValueCountFrequency (%)
1 350378
33.3%
. 350378
33.3%
0 350378
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 700756
66.7%
Other Punctuation 350378
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 350378
50.0%
0 350378
50.0%
Other Punctuation
ValueCountFrequency (%)
. 350378
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1051134
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 350378
33.3%
. 350378
33.3%
0 350378
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1051134
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 350378
33.3%
. 350378
33.3%
0 350378
33.3%

DATUM_BESTAND
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
Minimum2024-02-06 00:00:00
Maximum2024-02-06 00:00:00
2024-02-26T14:43:26.596956image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:26.925625image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

PEILDATUM
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
Minimum2024-02-01 00:00:00
Maximum2024-02-01 00:00:00
2024-02-26T14:43:27.048442image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:27.181352image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

JAAR
Date

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
Minimum2012-01-01 00:00:00
Maximum2024-01-01 00:00:00
2024-02-26T14:43:27.314719image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:27.482071image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)

BEHANDELEND_SPECIALISME_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean451.74501
Minimum301
Maximum8418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-02-26T14:43:27.658991image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum301
5-th percentile302
Q1305
median313
Q3322
95-th percentile361
Maximum8418
Range8117
Interquartile range (IQR)17

Descriptive statistics

Standard deviation1041.6907
Coefficient of variation (CV)2.3059263
Kurtosis54.40075
Mean451.74501
Median Absolute Deviation (MAD)8
Skewness7.5052894
Sum1.5828151 × 108
Variance1085119.5
MonotonicityNot monotonic
2024-02-26T14:43:27.848209image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
305 49275
14.1%
313 45475
13.0%
303 40309
11.5%
330 27550
 
7.9%
316 23797
 
6.8%
308 19099
 
5.5%
306 14724
 
4.2%
324 14418
 
4.1%
301 13953
 
4.0%
304 11361
 
3.2%
Other values (18) 90417
25.8%
ValueCountFrequency (%)
301 13953
 
4.0%
302 7693
 
2.2%
303 40309
11.5%
304 11361
 
3.2%
305 49275
14.1%
306 14724
 
4.2%
307 6148
 
1.8%
308 19099
 
5.5%
310 3815
 
1.1%
313 45475
13.0%
ValueCountFrequency (%)
8418 4695
 
1.3%
8416 1186
 
0.3%
1900 231
 
0.1%
390 960
 
0.3%
389 3673
 
1.0%
362 4487
 
1.3%
361 2551
 
0.7%
335 3528
 
1.0%
330 27550
7.9%
329 909
 
0.3%
Distinct1904
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
2024-02-26T14:43:28.246971image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.3534982
Min length2

Characters and Unicode

Total characters1174992
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st row506
2nd row12
3rd row11
4th row11
5th row12
ValueCountFrequency (%)
101 1496
 
0.4%
402 1435
 
0.4%
301 1406
 
0.4%
403 1404
 
0.4%
201 1333
 
0.4%
203 1304
 
0.4%
401 1170
 
0.3%
404 1164
 
0.3%
802 1137
 
0.3%
409 1131
 
0.3%
Other values (1894) 337398
96.3%
2024-02-26T14:43:28.876879image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 224681
19.1%
0 215914
18.4%
2 155768
13.3%
3 127029
10.8%
5 90705
7.7%
9 84552
 
7.2%
4 83109
 
7.1%
7 69285
 
5.9%
6 61403
 
5.2%
8 50664
 
4.3%
Other values (15) 11882
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1163110
99.0%
Uppercase Letter 11882
 
1.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 2205
18.6%
M 2008
16.9%
B 1433
12.1%
Z 1034
8.7%
E 1000
8.4%
D 784
 
6.6%
A 772
 
6.5%
F 733
 
6.2%
C 391
 
3.3%
K 381
 
3.2%
Other values (5) 1141
9.6%
Decimal Number
ValueCountFrequency (%)
1 224681
19.3%
0 215914
18.6%
2 155768
13.4%
3 127029
10.9%
5 90705
7.8%
9 84552
 
7.3%
4 83109
 
7.1%
7 69285
 
6.0%
6 61403
 
5.3%
8 50664
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1163110
99.0%
Latin 11882
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 2205
18.6%
M 2008
16.9%
B 1433
12.1%
Z 1034
8.7%
E 1000
8.4%
D 784
 
6.6%
A 772
 
6.5%
F 733
 
6.2%
C 391
 
3.3%
K 381
 
3.2%
Other values (5) 1141
9.6%
Common
ValueCountFrequency (%)
1 224681
19.3%
0 215914
18.6%
2 155768
13.4%
3 127029
10.9%
5 90705
7.8%
9 84552
 
7.3%
4 83109
 
7.1%
7 69285
 
6.0%
6 61403
 
5.3%
8 50664
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1174992
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 224681
19.1%
0 215914
18.4%
2 155768
13.3%
3 127029
10.8%
5 90705
7.7%
9 84552
 
7.2%
4 83109
 
7.1%
7 69285
 
5.9%
6 61403
 
5.2%
8 50664
 
4.3%
Other values (15) 11882
 
1.0%

ZORGPRODUCT_CD
Real number (ℝ)

Distinct6233
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4165927 × 108
Minimum10501002
Maximum9.9841808 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-02-26T14:43:29.103671image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum10501002
5-th percentile28999040
Q199899012
median1.49899 × 108
Q39.9000302 × 108
95-th percentile9.9051605 × 108
Maximum9.9841808 × 108
Range9.8791708 × 108
Interquartile range (IQR)8.9010401 × 108

Descriptive statistics

Standard deviation4.291256 × 108
Coefficient of variation (CV)0.97162139
Kurtosis-1.741246
Mean4.4165927 × 108
Median Absolute Deviation (MAD)1.199 × 108
Skewness0.46354923
Sum1.5474769 × 1014
Variance1.8414878 × 1017
MonotonicityNot monotonic
2024-02-26T14:43:29.321976image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
990004009 2539
 
0.7%
990004007 2500
 
0.7%
990003004 2437
 
0.7%
990004006 2047
 
0.6%
990356076 1857
 
0.5%
990356073 1730
 
0.5%
131999228 1710
 
0.5%
131999164 1685
 
0.5%
990003007 1574
 
0.4%
131999194 1539
 
0.4%
Other values (6223) 330760
94.4%
ValueCountFrequency (%)
10501002 9
< 0.1%
10501003 12
< 0.1%
10501004 12
< 0.1%
10501005 12
< 0.1%
10501007 3
 
< 0.1%
10501008 12
< 0.1%
10501010 12
< 0.1%
10501011 4
 
< 0.1%
11101002 11
< 0.1%
11101003 12
< 0.1%
ValueCountFrequency (%)
998418081 179
0.1%
998418080 163
< 0.1%
998418079 40
 
< 0.1%
998418077 9
 
< 0.1%
998418076 9
 
< 0.1%
998418075 7
 
< 0.1%
998418074 240
0.1%
998418073 241
0.1%
998418072 9
 
< 0.1%
998418071 9
 
< 0.1%

AANTAL_PAT_PER_ZPD
Real number (ℝ)

HIGH CORRELATION 

Distinct10613
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean520.77582
Minimum1
Maximum166129
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-02-26T14:43:29.528803image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median14
Q3105
95-th percentile1772
Maximum166129
Range166128
Interquartile range (IQR)102

Descriptive statistics

Standard deviation3206.8843
Coefficient of variation (CV)6.1578979
Kurtosis408.48179
Mean520.77582
Median Absolute Deviation (MAD)13
Skewness16.685963
Sum1.8246839 × 108
Variance10284107
MonotonicityNot monotonic
2024-02-26T14:43:29.735856image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 57743
 
16.5%
2 28177
 
8.0%
3 18475
 
5.3%
4 13508
 
3.9%
5 10521
 
3.0%
6 8934
 
2.5%
7 7491
 
2.1%
8 6286
 
1.8%
9 5663
 
1.6%
10 5113
 
1.5%
Other values (10603) 188467
53.8%
ValueCountFrequency (%)
1 57743
16.5%
2 28177
8.0%
3 18475
 
5.3%
4 13508
 
3.9%
5 10521
 
3.0%
6 8934
 
2.5%
7 7491
 
2.1%
8 6286
 
1.8%
9 5663
 
1.6%
10 5113
 
1.5%
ValueCountFrequency (%)
166129 1
< 0.1%
165184 1
< 0.1%
163737 1
< 0.1%
155869 1
< 0.1%
154640 1
< 0.1%
154258 1
< 0.1%
144714 1
< 0.1%
118396 1
< 0.1%
115934 1
< 0.1%
113287 1
< 0.1%

AANTAL_SUBTRAJECT_PER_ZPD
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct11468
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean617.67745
Minimum1
Maximum240002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-02-26T14:43:29.937683image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median15
Q3115
95-th percentile2026
Maximum240002
Range240001
Interquartile range (IQR)112

Descriptive statistics

Standard deviation4143.4464
Coefficient of variation (CV)6.708107
Kurtosis713.24932
Mean617.67745
Median Absolute Deviation (MAD)14
Skewness21.205653
Sum2.1642059 × 108
Variance17168148
MonotonicityNot monotonic
2024-02-26T14:43:30.146760image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 55575
 
15.9%
2 27673
 
7.9%
3 18298
 
5.2%
4 13297
 
3.8%
5 10426
 
3.0%
6 8919
 
2.5%
7 7414
 
2.1%
8 6216
 
1.8%
9 5623
 
1.6%
10 5094
 
1.5%
Other values (11458) 191843
54.8%
ValueCountFrequency (%)
1 55575
15.9%
2 27673
7.9%
3 18298
 
5.2%
4 13297
 
3.8%
5 10426
 
3.0%
6 8919
 
2.5%
7 7414
 
2.1%
8 6216
 
1.8%
9 5623
 
1.6%
10 5094
 
1.5%
ValueCountFrequency (%)
240002 1
< 0.1%
232423 1
< 0.1%
231954 1
< 0.1%
230943 1
< 0.1%
227936 1
< 0.1%
227409 1
< 0.1%
226679 1
< 0.1%
223891 1
< 0.1%
218673 1
< 0.1%
215131 1
< 0.1%

AANTAL_PAT_PER_DIAG
Real number (ℝ)

HIGH CORRELATION 

Distinct9616
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7816.3831
Minimum1
Maximum237926
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-02-26T14:43:30.343097image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile42
Q1414
median1763
Q36518
95-th percentile37209
Maximum237926
Range237925
Interquartile range (IQR)6104

Descriptive statistics

Standard deviation18045.087
Coefficient of variation (CV)2.3086237
Kurtosis34.337454
Mean7816.3831
Median Absolute Deviation (MAD)1605.5
Skewness5.0605358
Sum2.7386887 × 109
Variance3.2562516 × 108
MonotonicityNot monotonic
2024-02-26T14:43:30.542458image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 582
 
0.2%
25 553
 
0.2%
17 518
 
0.1%
8 498
 
0.1%
12 491
 
0.1%
26 488
 
0.1%
9 483
 
0.1%
14 479
 
0.1%
19 478
 
0.1%
4 472
 
0.1%
Other values (9606) 345336
98.6%
ValueCountFrequency (%)
1 385
0.1%
2 432
0.1%
3 422
0.1%
4 472
0.1%
5 430
0.1%
6 430
0.1%
7 452
0.1%
8 498
0.1%
9 483
0.1%
10 394
0.1%
ValueCountFrequency (%)
237926 22
< 0.1%
232873 23
< 0.1%
227997 23
< 0.1%
218547 24
< 0.1%
214503 17
< 0.1%
213515 25
< 0.1%
211576 17
< 0.1%
210414 19
< 0.1%
205337 17
< 0.1%
200599 16
< 0.1%

AANTAL_SUBTRAJECT_PER_DIAG
Real number (ℝ)

HIGH CORRELATION 

Distinct10672
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11348.518
Minimum1
Maximum370534
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-02-26T14:43:30.735293image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile54
Q1553
median2450
Q39345
95-th percentile53283
Maximum370534
Range370533
Interquartile range (IQR)8792

Descriptive statistics

Standard deviation27099.947
Coefficient of variation (CV)2.3879722
Kurtosis37.728885
Mean11348.518
Median Absolute Deviation (MAD)2249
Skewness5.297246
Sum3.9762712 × 109
Variance7.3440711 × 108
MonotonicityNot monotonic
2024-02-26T14:43:30.942506image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 450
 
0.1%
21 407
 
0.1%
23 401
 
0.1%
6 383
 
0.1%
13 382
 
0.1%
17 375
 
0.1%
38 373
 
0.1%
39 371
 
0.1%
4 371
 
0.1%
20 369
 
0.1%
Other values (10662) 346496
98.9%
ValueCountFrequency (%)
1 291
0.1%
2 320
0.1%
3 345
0.1%
4 371
0.1%
5 349
0.1%
6 383
0.1%
7 338
0.1%
8 335
0.1%
9 301
0.1%
10 339
0.1%
ValueCountFrequency (%)
370534 22
< 0.1%
370296 23
< 0.1%
370137 23
< 0.1%
348482 25
< 0.1%
344911 24
< 0.1%
341651 19
< 0.1%
323753 20
< 0.1%
315768 17
< 0.1%
310748 17
< 0.1%
298625 17
< 0.1%

AANTAL_PAT_PER_SPC
Real number (ℝ)

HIGH CORRELATION 

Distinct328
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean678821.96
Minimum1
Maximum1487627
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-02-26T14:43:31.149679image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile43684
Q1298229
median753456
Q31026244
95-th percentile1332263
Maximum1487627
Range1487626
Interquartile range (IQR)728015

Descriptive statistics

Standard deviation409874.74
Coefficient of variation (CV)0.603803
Kurtosis-1.0837827
Mean678821.96
Median Absolute Deviation (MAD)307087
Skewness-0.027678339
Sum2.3784428 × 1011
Variance1.679973 × 1011
MonotonicityNot monotonic
2024-02-26T14:43:31.360221image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
880922 5102
 
1.5%
874080 4354
 
1.2%
843976 4347
 
1.2%
894296 4333
 
1.2%
880450 4273
 
1.2%
897686 4212
 
1.2%
765004 4089
 
1.2%
803748 4036
 
1.2%
804298 4031
 
1.2%
1059670 3914
 
1.1%
Other values (318) 307687
87.8%
ValueCountFrequency (%)
1 1
 
< 0.1%
16 3
 
< 0.1%
83 4
 
< 0.1%
1610 130
< 0.1%
1829 138
< 0.1%
1919 131
< 0.1%
2495 173
< 0.1%
2556 190
0.1%
4386 179
0.1%
5635 131
< 0.1%
ValueCountFrequency (%)
1487627 2975
0.8%
1450389 3048
0.9%
1421695 3564
1.0%
1344187 3543
1.0%
1340492 3441
1.0%
1332263 3545
1.0%
1316758 3401
1.0%
1316275 3463
1.0%
1282929 3576
1.0%
1268011 3351
1.0%

AANTAL_SUBTRAJECT_PER_SPC
Real number (ℝ)

HIGH CORRELATION 

Distinct328
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1103154.4
Minimum1
Maximum2668756
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-02-26T14:43:31.572818image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile46632
Q1504900
median1107060
Q31810468
95-th percentile2593128
Maximum2668756
Range2668755
Interquartile range (IQR)1305568

Descriptive statistics

Standard deviation741410.45
Coefficient of variation (CV)0.67208223
Kurtosis-0.75292098
Mean1103154.4
Median Absolute Deviation (MAD)620829
Skewness0.35927552
Sum3.8652102 × 1011
Variance5.4968945 × 1011
MonotonicityNot monotonic
2024-02-26T14:43:31.786745image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1211798 5102
 
1.5%
1281474 4354
 
1.2%
1216250 4347
 
1.2%
1315553 4333
 
1.2%
1300410 4273
 
1.2%
1341797 4212
 
1.2%
1155933 4089
 
1.2%
1209711 4036
 
1.2%
1207087 4031
 
1.2%
2617416 3914
 
1.1%
Other values (318) 307687
87.8%
ValueCountFrequency (%)
1 1
 
< 0.1%
16 3
 
< 0.1%
142 4
 
< 0.1%
1861 130
< 0.1%
2097 138
< 0.1%
2194 131
< 0.1%
2816 173
< 0.1%
3325 190
0.1%
5261 179
0.1%
6291 131
< 0.1%
ValueCountFrequency (%)
2668756 3796
1.1%
2663844 3866
1.1%
2618309 3788
1.1%
2617416 3914
1.1%
2593128 3843
1.1%
2547840 3890
1.1%
2479678 3851
1.1%
2178334 3757
1.1%
2062010 3811
1.1%
2051882 1168
 
0.3%

GEMIDDELDE_VERKOOPPRIJS
Real number (ℝ)

MISSING 

Distinct3693
Distinct (%)1.2%
Missing53629
Missing (%)15.3%
Infinite0
Infinite (%)0.0%
Mean3629.5485
Minimum70
Maximum287220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 MiB
2024-02-26T14:43:31.986648image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile140
Q1480
median1270
Q34210
95-th percentile13825
Maximum287220
Range287150
Interquartile range (IQR)3730

Descriptive statistics

Standard deviation6561.8258
Coefficient of variation (CV)1.8078904
Kurtosis133.98854
Mean3629.5485
Median Absolute Deviation (MAD)1040
Skewness6.8956274
Sum1.0770649 × 109
Variance43057558
MonotonicityNot monotonic
2024-02-26T14:43:32.372860image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160 2090
 
0.6%
105 1982
 
0.6%
110 1792
 
0.5%
180 1626
 
0.5%
185 1571
 
0.4%
140 1548
 
0.4%
125 1466
 
0.4%
175 1433
 
0.4%
300 1427
 
0.4%
165 1381
 
0.4%
Other values (3683) 280433
80.0%
(Missing) 53629
 
15.3%
ValueCountFrequency (%)
70 226
 
0.1%
75 75
 
< 0.1%
80 362
 
0.1%
85 919
0.3%
90 670
 
0.2%
95 716
 
0.2%
100 1025
0.3%
105 1982
0.6%
110 1792
0.5%
115 1176
0.3%
ValueCountFrequency (%)
287220 8
< 0.1%
148910 3
 
< 0.1%
142835 4
< 0.1%
122155 4
< 0.1%
116765 3
 
< 0.1%
109725 7
< 0.1%
108570 7
< 0.1%
107655 4
< 0.1%
101270 8
< 0.1%
99580 5
< 0.1%

Interactions

2024-02-26T14:43:23.475812image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:11.360998image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:13.000945image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:14.448049image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:15.902983image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:17.342600image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:18.934870image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:20.468621image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:21.995970image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:23.654580image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:11.660033image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:13.171126image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:14.622531image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:16.073657image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:17.516707image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:19.113101image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:20.650740image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:22.168604image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:23.815503image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:11.824201image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:13.326336image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:14.778074image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:16.227883image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:17.670483image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:19.280050image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:20.813576image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:22.330083image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:23.976872image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:11.992317image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:13.485975image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:14.935552image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:16.391248image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:17.825919image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:19.456269image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:20.982616image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:22.497269image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:24.134395image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:12.155366image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:13.642446image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:15.089395image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:16.547173image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:17.974248image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:19.623467image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:21.146108image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:22.656585image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:24.289574image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:12.313749image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:13.790670image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:15.239469image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:16.692666image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:18.119924image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:19.780062image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:21.307499image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:22.812419image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:24.468193image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:12.490102image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:13.958843image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:15.410865image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:16.857075image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:18.284777image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:19.949639image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:21.487340image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:22.978677image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:24.644980image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:12.666291image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:14.125204image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:15.582844image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:17.020243image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:18.457814image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:20.125244image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:21.664610image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:23.145920image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:24.804903image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:12.831324image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:14.287351image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:15.741300image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:17.177252image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:18.617351image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:20.296345image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:21.827619image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-26T14:43:23.306319image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2024-02-26T14:43:32.516328image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
AANTAL_PAT_PER_DIAGAANTAL_PAT_PER_SPCAANTAL_PAT_PER_ZPDAANTAL_SUBTRAJECT_PER_DIAGAANTAL_SUBTRAJECT_PER_SPCAANTAL_SUBTRAJECT_PER_ZPDBEHANDELEND_SPECIALISME_CDGEMIDDELDE_VERKOOPPRIJSZORGPRODUCT_CD
AANTAL_PAT_PER_DIAG1.0000.3170.3210.9870.2980.318-0.0610.028-0.179
AANTAL_PAT_PER_SPC0.3171.0000.0700.3310.9600.073-0.551-0.013-0.380
AANTAL_PAT_PER_ZPD0.3210.0701.0000.3200.0790.9960.008-0.303-0.140
AANTAL_SUBTRAJECT_PER_DIAG0.9870.3310.3201.0000.3290.320-0.0550.036-0.211
AANTAL_SUBTRAJECT_PER_SPC0.2980.9600.0790.3291.0000.086-0.470-0.015-0.408
AANTAL_SUBTRAJECT_PER_ZPD0.3180.0730.9960.3200.0861.0000.013-0.306-0.149
BEHANDELEND_SPECIALISME_CD-0.061-0.5510.008-0.055-0.4700.0131.0000.0460.214
GEMIDDELDE_VERKOOPPRIJS0.028-0.013-0.3030.036-0.015-0.3060.0461.0000.029
ZORGPRODUCT_CD-0.179-0.380-0.140-0.211-0.408-0.1490.2140.0291.000

Missing values

2024-02-26T14:43:25.058063image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-26T14:43:25.607029image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

VERSIEDATUM_BESTANDPEILDATUMJAARBEHANDELEND_SPECIALISME_CDTYPERENDE_DIAGNOSE_CDZORGPRODUCT_CDAANTAL_PAT_PER_ZPDAANTAL_SUBTRAJECT_PER_ZPDAANTAL_PAT_PER_DIAGAANTAL_SUBTRAJECT_PER_DIAGAANTAL_PAT_PER_SPCAANTAL_SUBTRAJECT_PER_SPCGEMIDDELDE_VERKOOPPRIJS
01.02024-02-062024-02-012024-01-018418506998418070111111NaN
11.02024-02-062024-02-012019-01-0119001299190001836494042177532286179304103931440.0
21.02024-02-062024-02-012019-01-011900119919000221259713886619158034079304103931825.0
31.02024-02-062024-02-012019-01-0119001199190000322619158034079304103931475.0
41.02024-02-062024-02-012019-01-0119001299190000825812655177532286179304103931460.0
51.02024-02-062024-02-012019-01-011900139919000262372173079304103931115.0
61.02024-02-062024-02-012019-01-0119001199190002438014066619158034079304103931305.0
71.02024-02-062024-02-012019-01-011900119919000261279513710619158034079304103931115.0
81.02024-02-062024-02-012019-01-01190012991900014309431091775322861793041039312085.0
91.02024-02-062024-02-012019-01-011900119919000254087847362619158034079304103931290.0
VERSIEDATUM_BESTANDPEILDATUMJAARBEHANDELEND_SPECIALISME_CDTYPERENDE_DIAGNOSE_CDZORGPRODUCT_CDAANTAL_PAT_PER_ZPDAANTAL_SUBTRAJECT_PER_ZPDAANTAL_PAT_PER_DIAGAANTAL_SUBTRAJECT_PER_DIAGAANTAL_PAT_PER_SPCAANTAL_SUBTRAJECT_PER_SPCGEMIDDELDE_VERKOOPPRIJS
3503681.02024-02-062024-02-012016-01-01307B1715989901044273070131211856014125.0
3503691.02024-02-062024-02-012020-01-013136292909903635194352104684126183091110.0
3503701.02024-02-062024-02-012014-01-0130321119929905833963211143142169518455924700.0
3503711.02024-02-062024-02-012020-01-01313522131999021661482306910468412618309NaN
3503721.02024-02-062024-02-012016-01-01307B1415989900422828879701312118560110285.0
3503731.02024-02-062024-02-012014-01-01303280199299089228390387747142169518455923570.0
3503741.02024-02-062024-02-012020-01-01313932119499056112338292710468412618309NaN
3503751.02024-02-062024-02-012020-01-013130949900350021113414010468412618309275.0
3503761.02024-02-062024-02-012013-01-01326G499002601711112229926173490.0
3503771.02024-02-062024-02-012020-01-0131392411949907211107312221046841261830922290.0